模拟退火旅行商问题Python可视化

好的，以下是一个简单的模拟退火旅行商问题的Python可视化代码：

import random
import math
import matplotlib.pyplot as plt

def distance(city1, city2):
    """计算两个城市之间的距离"""
    x1, y1 = city1
    x2, y2 = city2
    return math.sqrt((x1-x2)**2 + (y1-y2)**2)

def total_distance(route):
    """计算路径的总长度"""
    dist = 0
    for i in range(1, len(route)):
        dist += distance(route[i-1], route[i])
    dist += distance(route[-1], route[0])
    return dist

def simulated_annealing(cities, temperature=1000, cooling_rate=0.003):
    """模拟退火算法解决旅行商问题"""
    current_route = random.sample(cities, len(cities))
    best_route = current_route[:]
    while temperature > 1:
        # 随机交换两个城市的位置
        i, j = random.sample(range(len(cities)), 2)
        current_route[i], current_route[j] = current_route[j], current_route[i]
        # 计算新路径的长度
        current_distance = total_distance(current_route)
        best_distance = total_distance(best_route)
        # 判断是否接受新路径
        if current_distance < best_distance or math.exp((best_distance-current_distance)/temperature) > random.random():
            best_route = current_route[:]
        # 降温
        temperature *= 1 - cooling_rate
    return best_route

def plot_route(cities, route):
    """绘制城市和路径"""
    x = [city[0] for city in cities]
    y = [city[1] for city in cities]
    plt.scatter(x, y, color='red')
    for i in range(1, len(route)):
        city1 = route[i-1]
        city2 = route[i]
        plt.plot([city1[0], city2[0]], [city1[1], city2[1]], color='blue')
    city1 = route[-1]
    city2 = route[0]
    plt.plot([city1[0], city2[0]], [city1[1], city2[1]], color='blue')
    plt.show()

# 生成随机城市
cities = [(random.uniform(0, 100), random.uniform(0, 100)) for i in range(20)]
# 使用模拟退火算法求解旅行商问题
best_route = simulated_annealing(cities)
# 绘制城市和路径
plot_route(cities, best_route)

运行代码后，将会生成一个随机城市和路径的可视化图像。其中红色点表示城市，蓝色线段表示路径。模拟退火算法会尝试不断优化路径，使得路径长度最小化。你可以尝试改变生成的随机城市数量和算法的参数，观察算法的效果和可视化结果。